An Automated Algorithm for Determining Photometric Redshifts of Quasars
Identifieur interne : 000B49 ( Main/Exploration ); précédent : 000B48; suivant : 000B50An Automated Algorithm for Determining Photometric Redshifts of Quasars
Auteurs : DAN WANG [République populaire de Chine] ; YANXIA ZHANG [République populaire de Chine] ; YONGHENG ZHAO [République populaire de Chine]Source :
- Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2010.
Descripteurs français
- Pascal (Inist)
- Wicri :
- topic : Intelligence artificielle.
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Abstract
We employ k-nearest neighbor algorithm (KNN) for photometric redshift measurement of quasars with the Fifth Data Release (DR5) of the Sloan Digital Sky Survey (SDSS). KNN is an instance learning algorithm where the result of new instance query is predicted based on the closest training samples. The regressor do not use any model to fit and only based on memory. Given a query quasar, we find the known quasars or (training points) closest to the query point, whose redshift value is simply assigned to be the average of the values of its k nearest neighbors. Three kinds of different colors (PSF, Model or Fiber) and spectral redshifts are used as input parameters, separatively. The combination of the three kinds of colors is also taken as input. The experimental results indicate that the best input pattern is PSF + Model + Fiber colors in all experiments. With this pattern, 59.24%, 77.34% and 84.68% of photometric redshifts are obtained within Δz < 0.1, 0.2 and 0.3, respectively. If only using one kind of colors as input, the model colors achieve the best performance. However, when using two kinds of colors, the best result is achieved by PSF + Fiber colors. In addition, nearest neighbor method (k = 1) shows its superiority compared to KNN (k ¬= 1) for the given sample.
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SPIE Int. Soc. Opt. Eng.</title><idno type="ISSN">0277-786X</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Proceedings of SPIE, the International Society for Optical Engineering</title><title level="j" type="abbreviated">Proc. SPIE Int. Soc. Opt. Eng.</title><idno type="ISSN">0277-786X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial intelligence</term><term>Astrophysics</term><term>Learning algorithm</term><term>Model matching</term><term>Modeling</term><term>Nearest neighbour</term><term>Photometry</term><term>Point spread function</term><term>Sky</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Plus proche voisin</term><term>Intelligence artificielle</term><term>Photométrie</term><term>Astrophysique</term><term>Ciel</term><term>Algorithme apprentissage</term><term>Ajustement modèle</term><term>Fonction étalement point</term><term>Modélisation</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Intelligence artificielle</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We employ k-nearest neighbor algorithm (KNN) for photometric redshift measurement of quasars with the Fifth Data Release (DR5) of the Sloan Digital Sky Survey (SDSS). KNN is an instance learning algorithm where the result of new instance query is predicted based on the closest training samples. The regressor do not use any model to fit and only based on memory. Given a query quasar, we find the known quasars or (training points) closest to the query point, whose redshift value is simply assigned to be the average of the values of its k nearest neighbors. Three kinds of different colors (PSF, Model or Fiber) and spectral redshifts are used as input parameters, separatively. The combination of the three kinds of colors is also taken as input. The experimental results indicate that the best input pattern is PSF + Model + Fiber colors in all experiments. With this pattern, 59.24%, 77.34% and 84.68% of photometric redshifts are obtained within Δz < 0.1, 0.2 and 0.3, respectively. If only using one kind of colors as input, the model colors achieve the best performance. However, when using two kinds of colors, the best result is achieved by PSF + Fiber colors. In addition, nearest neighbor method (k = 1) shows its superiority compared to KNN (k ¬= 1) for the given sample.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Dan Wang" sort="Dan Wang" uniqKey="Dan Wang" last="Dan Wang">DAN WANG</name></noRegion><name sortKey="Yanxia Zhang" sort="Yanxia Zhang" uniqKey="Yanxia Zhang" last="Yanxia Zhang">YANXIA ZHANG</name><name sortKey="Yongheng Zhao" sort="Yongheng Zhao" uniqKey="Yongheng Zhao" last="Yongheng Zhao">YONGHENG ZHAO</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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